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Record W3035000917 · doi:10.2196/16351

Web-Based Health Coaching for Spinal Cord Injury: Results From a Mixed Methods Feasibility Evaluation

2020· article· en· W3035000917 on OpenAlexafffundvenue
Sonya Allin, John D. Shepherd, Teri Thorson, Jennifer R. Tomasone, Sarah Munce, Gary Linassi, Christopher B. McBride, Tizneem Jiancaro, Susan Jaglal

Bibliographic record

VenueJMIR Rehabilitation and Assistive Technologies · 2020
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsUniversity of SaskatchewanQueen's UniversitySpinal Cord Injury BCToronto Rehabilitation InstituteUniversity of Toronto
FundersOntario Neurotrauma FoundationRick Hansen Institute
KeywordsSpinal cord injuryMedicineCoachingContext (archaeology)MoodPhysical therapyQuality of life (healthcare)Self-efficacyPsychologyClinical psychologyNursingPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals with spinal cord injury (SCI) are at high risk of experiencing secondary conditions like pressure injuries. Self-management programs may reduce the risk of complications, but traditional programs have proven to be insufficiently tailored to the needs of people with SCI. To overcome barriers to self-management support, a web-based, self-management program was developed for Canadians with SCI called SCI & U. OBJECTIVE: This study aims to evaluate the feasibility and potential impact of the SCI & U program in the context of a mixed methods pilot study. METHODS: The study followed an explanatory, sequential mixed methods design. Participants (N=11) were Canadians with SCI who had been living in the community for more than 1 year. Each took part in a self-paced, six-session self-management program guided by a trained peer health coach. During sessions, participants could discuss a health topic with their coach from a predefined list (eg, skin or bowel management). Quantitative data were gathered before and after program participation to assess program feasibility and impact. Feasibility measures included attrition rates, frequency of topics selected, and recorded goals, whereas impact measures included measures of self-efficacy (University of Washington Self-Efficacy Scale [UW-SES]), mood (Personal Health Questionnaire Depression Scale [PHQ-8]), secondary conditions (Spinal Cord Injury Secondary Conditions Scale [SCI-SCS]), and resilience (Spinal Cord Injury Quality of Life Resilience Scale [SCI-QOL-R]). Qualitative measures were based on postintervention interviews; these were designed to confirm and expand on quantitative. RESULTS: Of the 11 participants, 10 completed pre- and postassessments, and 6 coaching sessions. Sessions lasted between 31 and 81 min (average 55, SD 13), and the duration of the program ranged from 35 to 88 days (average 56, SD 23). Diet and exercise were selected as topics 40% (20/50 sessions with topics) of the time, whereas topics such as mental health, bladder management, pain, and bowel management were chosen less frequently. Results gathered before and after the pilot study demonstrated improvements with moderate effect sizes on the UW-SES and the electronic health literacy scale (ie, Hedges g>0.5). Effect sizes for measures of resilience (SCI-QOL-R), depression (PHQ-8), and secondary conditions (SCI-SCS) were small (ie, Hedges g>0.3). Qualitative results confirmed a common focus on diet and exercise, and defined coaches as sources of accountability, information, reassurance and affirmation, and emotional and technical support. CONCLUSIONS: Results demonstrated that a web-based self-management program is feasible and acceptable by Canadians with SCI. Results also indicated a web-based, peer-led self-management program may impact resilience, self-efficacy, mood, and secondary complications. Finally, results illuminated the role of the coach in facilitating behavior change. Future work seeks to validate results in the context of a randomized controlled trial.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.029
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.171
GPT teacher head0.518
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations34
Published2020
Admission routes3
Has abstractyes

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